Delete artifacts

artifacts/llm_output_gpu0_seed1.json

@@ -1,18 +0,0 @@
-{
-  "analysis": "High image_similarity (0.86) and low reconstruction entropy (0.156) indicate that X preserves the structural manifold defined by Y — modes are intact and there is no structural degradation. Learning is active (learning_signal 0.328, learning_active = true, stagnation = false) while the system is heating slightly (temp = 0.923, temp_slope > 0). Mixing shows mixed signals: r_hat ~1.008 (near ideal) but tau_int ~6.9 (slow autocorrelation). Quality metrics (low mean L2, high effective rank) support good reconstruction and representational capacity. Given the strong structural similarity, the STRICT CONSTRAINT forbids labeling this as mode collapse despite somewhat slow mixing and low entropy. Overall: the system is actively learning and producing ordered, structurally faithful outputs. Confidence is bounded by the strong image_similarity evidence but tempered by limited temporal-history detail and the slow mixing signal. [OVERRIDDEN: high structural similarity]",
-  "regime": "learning",
-  "phase": "ordered",
-  "scores": {
-    "temperature": 0.46,
-    "gibbs": 0.36
-  },
-  "risk": {
-    "stagnation": 0.05,
-    "collapse": 0.0,
-    "over_ordering": 0.12
-  },
-  "confidence": 0.2719535708427429,
-  "evidence": 0.2719535708427429,
-  "llm_conf_raw": 0.72,
-  "reason": "low_confidence_scaled"
-}

artifacts/run.log

@@ -1,665 +0,0 @@
-(srtrbm_env) ...:~/srtrbm_project$ python3 srtrbm_project_core.py
-Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
-Loading model from: /home/gosachunt_uv0/srtrbm_env/lib/python3.10/site-packages/lpips/weights/v0.1/vgg.pth
-Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
-Loading model from: /home/gosachunt_uv0/srtrbm_env/lib/python3.10/site-packages/lpips/weights/v0.1/vgg.pth
-Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
-Loading model from: /home/gosachunt_uv0/srtrbm_env/lib/python3.10/site-packages/lpips/weights/v0.1/vgg.pth
-Training: 100%|██████████████████████████████████████| 400/400 [03:47<00:00,  1.76it/s, T=0.923, flip=0.055, beta=4.386]
-
-[GPU 0] Running AIS...
-✔ AIS finished in 93.88 seconds
-coefficient (kappa): 784
-coefficient (kappa): 784
-
-[LLM] Start | B=1200 | T=0.9234
-[Debug] ESS=1.45 | k=1198 | deltaE_band=[0.00,2.00] | in_band=121 | selected=121
-[Debug] d_model=1.4697, d_llm=0.3010
-[Step] Sample=11 | ACCEPT | d_model=1.470 | d_total=1.680 | p=0.186
-[Debug] d_model=0.5433, d_llm=0.3745
-[Step] Sample=12 | REJECT | d_model=0.543 | d_total=0.805 | p=0.447
-[Debug] d_model=1.6846, d_llm=0.1223
-[Step] Sample=57 | REJECT | d_model=1.685 | d_total=1.770 | p=0.170
-[Debug] d_model=1.7959, d_llm=-1.2222
-[Step] Sample=67 | REJECT | d_model=1.796 | d_total=0.940 | p=0.391
-[Debug] d_model=1.6711, d_llm=1.1323
-[Step] Sample=78 | REJECT | d_model=1.671 | d_total=2.464 | p=0.085
-[Debug] d_model=1.4273, d_llm=0.9681
-[Step] Sample=82 | REJECT | d_model=1.427 | d_total=2.105 | p=0.122
-[Debug] d_model=1.4122, d_llm=-0.9590
-[Step] Sample=91 | REJECT | d_model=1.412 | d_total=0.741 | p=0.477
-[Debug] d_model=1.8038, d_llm=-1.2158
-[Step] Sample=93 | REJECT | d_model=1.804 | d_total=0.953 | p=0.386
-[Debug] d_model=1.9671, d_llm=-0.3832
-[Step] Sample=106 | REJECT | d_model=1.967 | d_total=1.699 | p=0.183
-[Debug] d_model=1.0448, d_llm=-0.7018
-[Step] Sample=110 | REJECT | d_model=1.045 | d_total=0.554 | p=0.575
-[Debug] d_model=0.0450, d_llm=0.0307
-[Step] Sample=112 | ACCEPT | d_model=0.045 | d_total=0.067 | p=0.936
-[Debug] d_model=1.3608, d_llm=-0.9301
-[Step] Sample=162 | REJECT | d_model=1.361 | d_total=0.710 | p=0.492
-[Debug] d_model=1.7553, d_llm=0.1205
-[Step] Sample=164 | REJECT | d_model=1.755 | d_total=1.840 | p=0.159
-[Debug] d_model=1.5541, d_llm=-1.0534
-[Step] Sample=176 | ACCEPT | d_model=1.554 | d_total=0.817 | p=0.442
-[Debug] d_model=0.9720, d_llm=0.6645
-[Step] Sample=177 | REJECT | d_model=0.972 | d_total=1.437 | p=0.238
-[Debug] d_model=1.6449, d_llm=-0.1346
-[Step] Sample=188 | REJECT | d_model=1.645 | d_total=1.551 | p=0.212
-[Debug] d_model=1.6659, d_llm=-1.1296
-[Step] Sample=195 | ACCEPT | d_model=1.666 | d_total=0.875 | p=0.417
-[Debug] d_model=1.1694, d_llm=0.2184
-[Step] Sample=217 | REJECT | d_model=1.169 | d_total=1.322 | p=0.267
-[Debug] d_model=1.6186, d_llm=-1.1001
-[Step] Sample=224 | ACCEPT | d_model=1.619 | d_total=0.849 | p=0.428
-[Debug] d_model=0.2307, d_llm=0.1591
-[Step] Sample=229 | ACCEPT | d_model=0.231 | d_total=0.342 | p=0.710
-[Debug] d_model=1.2473, d_llm=0.5990
-[Step] Sample=235 | ACCEPT | d_model=1.247 | d_total=1.667 | p=0.189
-[Debug] d_model=0.1347, d_llm=-0.0947
-[Step] Sample=237 | REJECT | d_model=0.135 | d_total=0.068 | p=0.934
-[Debug] d_model=1.8738, d_llm=-0.1807
-[Step] Sample=259 | REJECT | d_model=1.874 | d_total=1.747 | p=0.174
-[Debug] d_model=0.2584, d_llm=0.1824
-[Step] Sample=266 | ACCEPT | d_model=0.258 | d_total=0.386 | p=0.680
-[Debug] d_model=1.1483, d_llm=-0.8146
-[Step] Sample=283 | ACCEPT | d_model=1.148 | d_total=0.578 | p=0.561
-[Debug] d_model=1.4394, d_llm=-1.0205
-[Step] Sample=285 | REJECT | d_model=1.439 | d_total=0.725 | p=0.484
-[Debug] d_model=1.4801, d_llm=1.0477
-[Step] Sample=287 | REJECT | d_model=1.480 | d_total=2.214 | p=0.109
-[Debug] d_model=0.8691, d_llm=-0.6211
-[Step] Sample=293 | REJECT | d_model=0.869 | d_total=0.434 | p=0.648
-[Debug] d_model=1.6245, d_llm=0.1131
-[Step] Sample=294 | ACCEPT | d_model=1.624 | d_total=1.704 | p=0.182
-[Debug] d_model=0.9158, d_llm=-0.6567
-[Step] Sample=295 | ACCEPT | d_model=0.916 | d_total=0.456 | p=0.634
-[Debug] d_model=0.7560, d_llm=0.5480
-[Step] Sample=300 | ACCEPT | d_model=0.756 | d_total=1.140 | p=0.320
-[Debug] d_model=1.5959, d_llm=-0.0621
-[Step] Sample=311 | REJECT | d_model=1.596 | d_total=1.552 | p=0.212
-[Debug] d_model=0.9891, d_llm=-0.7186
-[Step] Sample=320 | ACCEPT | d_model=0.989 | d_total=0.486 | p=0.615
-[Debug] d_model=0.8514, d_llm=-0.6244
-[Step] Sample=321 | REJECT | d_model=0.851 | d_total=0.414 | p=0.661
-[Debug] d_model=1.9300, d_llm=-1.3946
-[Step] Sample=330 | ACCEPT | d_model=1.930 | d_total=0.954 | p=0.385
-[Debug] d_model=1.8164, d_llm=0.7165
-[Step] Sample=334 | REJECT | d_model=1.816 | d_total=2.318 | p=0.098
-[Debug] d_model=1.2972, d_llm=0.1747
-[Step] Sample=339 | REJECT | d_model=1.297 | d_total=1.419 | p=0.242
-[Debug] d_model=1.0131, d_llm=-0.2784
-[Step] Sample=348 | ACCEPT | d_model=1.013 | d_total=0.818 | p=0.441
-[Debug] d_model=0.7736, d_llm=0.5640
-[Step] Sample=365 | REJECT | d_model=0.774 | d_total=1.168 | p=0.311
-[Debug] d_model=1.0912, d_llm=0.7783
-[Step] Sample=368 | REJECT | d_model=1.091 | d_total=1.636 | p=0.195
-[Debug] d_model=0.3195, d_llm=0.2376
-[Step] Sample=378 | REJECT | d_model=0.320 | d_total=0.486 | p=0.615
-[Debug] d_model=1.6549, d_llm=0.4173
-[Step] Sample=386 | REJECT | d_model=1.655 | d_total=1.947 | p=0.143
-[Debug] d_model=1.9336, d_llm=-0.4042
-[Step] Sample=388 | REJECT | d_model=1.934 | d_total=1.651 | p=0.192
-[Debug] d_model=0.6572, d_llm=0.4833
-[Step] Sample=392 | ACCEPT | d_model=0.657 | d_total=0.996 | p=0.370
-[Debug] d_model=1.2113, d_llm=0.0825
-[Step] Sample=420 | REJECT | d_model=1.211 | d_total=1.269 | p=0.281
-[Debug] d_model=0.5882, d_llm=0.4393
-[Step] Sample=423 | ACCEPT | d_model=0.588 | d_total=0.896 | p=0.408
-[Debug] d_model=1.6005, d_llm=0.5490
-[Step] Sample=429 | REJECT | d_model=1.600 | d_total=1.985 | p=0.137
-[Debug] d_model=0.2766, d_llm=0.2083
-[Step] Sample=432 | ACCEPT | d_model=0.277 | d_total=0.422 | p=0.655
-[Debug] d_model=0.2587, d_llm=-0.1976
-[Step] Sample=439 | ACCEPT | d_model=0.259 | d_total=0.120 | p=0.887
-[Debug] d_model=0.5103, d_llm=-0.3949
-[Step] Sample=449 | ACCEPT | d_model=0.510 | d_total=0.234 | p=0.792
-[Debug] d_model=0.1726, d_llm=0.1356
-[Step] Sample=454 | ACCEPT | d_model=0.173 | d_total=0.268 | p=0.765
-[Debug] d_model=1.3263, d_llm=-1.0407
-[Step] Sample=461 | ACCEPT | d_model=1.326 | d_total=0.598 | p=0.550
-[Debug] d_model=1.4614, d_llm=-1.1413
-[Step] Sample=468 | REJECT | d_model=1.461 | d_total=0.662 | p=0.516
-[Debug] d_model=0.3996, d_llm=0.3164
-[Step] Sample=477 | ACCEPT | d_model=0.400 | d_total=0.621 | p=0.537
-[Debug] d_model=1.1709, d_llm=0.9290
-[Step] Sample=488 | REJECT | d_model=1.171 | d_total=1.821 | p=0.162
-[Debug] d_model=0.1049, d_llm=0.0368
-[Step] Sample=493 | ACCEPT | d_model=0.105 | d_total=0.131 | p=0.878
-[Debug] d_model=1.0052, d_llm=-0.8140
-[Step] Sample=500 | ACCEPT | d_model=1.005 | d_total=0.435 | p=0.647
-[Debug] d_model=1.3840, d_llm=1.1164
-[Step] Sample=511 | REJECT | d_model=1.384 | d_total=2.165 | p=0.115
-[Debug] d_model=1.5587, d_llm=-1.2465
-[Step] Sample=518 | REJECT | d_model=1.559 | d_total=0.686 | p=0.504
-[Debug] d_model=1.3459, d_llm=-0.6399
-[Step] Sample=523 | REJECT | d_model=1.346 | d_total=0.898 | p=0.407
-[Debug] d_model=1.8494, d_llm=-1.4501
-[Step] Sample=580 | ACCEPT | d_model=1.849 | d_total=0.834 | p=0.434
-[Debug] d_model=0.8530, d_llm=0.0617
-[Step] Sample=606 | REJECT | d_model=0.853 | d_total=0.896 | p=0.408
-[Debug] d_model=0.9905, d_llm=-0.7878
-[Step] Sample=611 | ACCEPT | d_model=0.991 | d_total=0.439 | p=0.645
-[Debug] d_model=1.1063, d_llm=0.8828
-[Step] Sample=615 | ACCEPT | d_model=1.106 | d_total=1.724 | p=0.178
-[Debug] d_model=0.4996, d_llm=0.4038
-[Step] Sample=622 | REJECT | d_model=0.500 | d_total=0.782 | p=0.457
-[Debug] d_model=0.5797, d_llm=0.4741
-[Step] Sample=629 | REJECT | d_model=0.580 | d_total=0.912 | p=0.402
-[Debug] d_model=1.6797, d_llm=0.8356
-[Step] Sample=633 | REJECT | d_model=1.680 | d_total=2.265 | p=0.104
-[Debug] d_model=1.9124, d_llm=-0.7472
-[Step] Sample=647 | REJECT | d_model=1.912 | d_total=1.389 | p=0.249
-[Debug] d_model=0.3513, d_llm=0.2818
-[Step] Sample=648 | REJECT | d_model=0.351 | d_total=0.548 | p=0.578
-[Debug] d_model=0.3799, d_llm=-0.3090
-[Step] Sample=655 | REJECT | d_model=0.380 | d_total=0.164 | p=0.849
-[Debug] d_model=0.8253, d_llm=0.1241
-[Step] Sample=660 | REJECT | d_model=0.825 | d_total=0.912 | p=0.402
-[Debug] d_model=0.2017, d_llm=0.1679
-[Step] Sample=666 | ACCEPT | d_model=0.202 | d_total=0.319 | p=0.727
-[Debug] d_model=0.8784, d_llm=-0.7362
-[Step] Sample=676 | REJECT | d_model=0.878 | d_total=0.363 | p=0.696
-[Debug] d_model=1.6433, d_llm=0.9457
-[Step] Sample=677 | REJECT | d_model=1.643 | d_total=2.305 | p=0.100
-[Debug] d_model=1.6694, d_llm=-1.3625
-[Step] Sample=679 | REJECT | d_model=1.669 | d_total=0.716 | p=0.489
-[Debug] d_model=0.5307, d_llm=0.3288
-[Step] Sample=681 | ACCEPT | d_model=0.531 | d_total=0.761 | p=0.467
-[Debug] d_model=1.5447, d_llm=-0.9997
-[Step] Sample=698 | REJECT | d_model=1.545 | d_total=0.845 | p=0.430
-[Debug] d_model=1.5245, d_llm=1.2376
-[Step] Sample=706 | REJECT | d_model=1.524 | d_total=2.391 | p=0.092
-[Debug] d_model=1.0637, d_llm=0.8668
-[Step] Sample=709 | REJECT | d_model=1.064 | d_total=1.670 | p=0.188
-[Debug] d_model=0.2297, d_llm=-0.1899
-[Step] Sample=733 | ACCEPT | d_model=0.230 | d_total=0.097 | p=0.908
-[Debug] d_model=1.1642, d_llm=0.9638
-[Step] Sample=749 | REJECT | d_model=1.164 | d_total=1.839 | p=0.159
-[Debug] d_model=0.0324, d_llm=0.0272
-[Step] Sample=761 | ACCEPT | d_model=0.032 | d_total=0.051 | p=0.950
-[Debug] d_model=0.9646, d_llm=-0.8150
-[Step] Sample=770 | ACCEPT | d_model=0.965 | d_total=0.394 | p=0.674
-[Debug] d_model=0.1593, d_llm=0.1366
-[Step] Sample=771 | ACCEPT | d_model=0.159 | d_total=0.255 | p=0.775
-[Debug] d_model=1.5256, d_llm=-1.2947
-[Step] Sample=788 | ACCEPT | d_model=1.526 | d_total=0.619 | p=0.538
-[Debug] d_model=0.1009, d_llm=0.0869
-[Step] Sample=793 | ACCEPT | d_model=0.101 | d_total=0.162 | p=0.851
-[Debug] d_model=0.2913, d_llm=-0.2545
-[Step] Sample=795 | ACCEPT | d_model=0.291 | d_total=0.113 | p=0.893
-[Debug] d_model=1.5299, d_llm=-1.3215
-[Step] Sample=821 | ACCEPT | d_model=1.530 | d_total=0.605 | p=0.546
-[Debug] d_model=1.6298, d_llm=-0.3155
-[Step] Sample=842 | ACCEPT | d_model=1.630 | d_total=1.409 | p=0.244
-[Debug] d_model=0.2302, d_llm=-0.1988
-[Step] Sample=858 | ACCEPT | d_model=0.230 | d_total=0.091 | p=0.913
-[Debug] d_model=1.1273, d_llm=-0.5328
-[Step] Sample=860 | ACCEPT | d_model=1.127 | d_total=0.754 | p=0.470
-[Debug] d_model=1.4784, d_llm=1.2662
-[Step] Sample=867 | REJECT | d_model=1.478 | d_total=2.365 | p=0.094
-[Debug] d_model=1.9334, d_llm=1.1137
-[Step] Sample=878 | REJECT | d_model=1.933 | d_total=2.713 | p=0.066
-[Debug] d_model=1.1638, d_llm=-0.9725
-[Step] Sample=884 | ACCEPT | d_model=1.164 | d_total=0.483 | p=0.617
-[Debug] d_model=1.3986, d_llm=-0.3591
-[Step] Sample=899 | REJECT | d_model=1.399 | d_total=1.147 | p=0.318
-[Debug] d_model=1.2407, d_llm=1.0301
-[Step] Sample=919 | ACCEPT | d_model=1.241 | d_total=1.962 | p=0.141
-[Debug] d_model=0.9731, d_llm=-0.3391
-[Step] Sample=923 | REJECT | d_model=0.973 | d_total=0.736 | p=0.479
-[Debug] d_model=0.0852, d_llm=-0.0722
-[Step] Sample=948 | ACCEPT | d_model=0.085 | d_total=0.035 | p=0.966
-[Debug] d_model=1.5349, d_llm=1.2874
-[Step] Sample=958 | REJECT | d_model=1.535 | d_total=2.436 | p=0.088
-[Debug] d_model=0.9681, d_llm=0.8162
-[Step] Sample=960 | REJECT | d_model=0.968 | d_total=1.539 | p=0.214
-[Debug] d_model=1.8524, d_llm=-1.3622
-[Step] Sample=971 | REJECT | d_model=1.852 | d_total=0.899 | p=0.407
-[Debug] d_model=0.6808, d_llm=-0.5678
-[Step] Sample=977 | ACCEPT | d_model=0.681 | d_total=0.283 | p=0.753
-[Debug] d_model=1.8602, d_llm=-1.0958
-[Step] Sample=997 | REJECT | d_model=1.860 | d_total=1.093 | p=0.335
-[Debug] d_model=1.0773, d_llm=-0.1919
-[Step] Sample=1000 | ACCEPT | d_model=1.077 | d_total=0.943 | p=0.389
-[Debug] d_model=0.7668, d_llm=-0.6344
-[Step] Sample=1001 | REJECT | d_model=0.767 | d_total=0.323 | p=0.724
-[Debug] d_model=1.3425, d_llm=-1.1067
-[Step] Sample=1011 | REJECT | d_model=1.342 | d_total=0.568 | p=0.567
-[Debug] d_model=0.7520, d_llm=-0.6258
-[Step] Sample=1017 | ACCEPT | d_model=0.752 | d_total=0.314 | p=0.731
-[Debug] d_model=0.0990, d_llm=-0.0836
-[Step] Sample=1035 | ACCEPT | d_model=0.099 | d_total=0.040 | p=0.960
-[Debug] d_model=1.1023, d_llm=-0.9331
-[Step] Sample=1037 | ACCEPT | d_model=1.102 | d_total=0.449 | p=0.638
-[Debug] d_model=0.6620, d_llm=-0.5662
-[Step] Sample=1075 | ACCEPT | d_model=0.662 | d_total=0.266 | p=0.767
-[Debug] d_model=1.8959, d_llm=-1.5834
-[Step] Sample=1102 | REJECT | d_model=1.896 | d_total=0.788 | p=0.455
-[Debug] d_model=1.0834, d_llm=-0.9073
-[Step] Sample=1103 | ACCEPT | d_model=1.083 | d_total=0.448 | p=0.639
-[Debug] d_model=0.3075, d_llm=0.2612
-[Step] Sample=1113 | REJECT | d_model=0.307 | d_total=0.490 | p=0.612
-[Debug] d_model=1.6499, d_llm=-0.4457
-[Step] Sample=1124 | ACCEPT | d_model=1.650 | d_total=1.338 | p=0.262
-[Debug] d_model=1.1920, d_llm=-0.9982
-[Step] Sample=1132 | ACCEPT | d_model=1.192 | d_total=0.493 | p=0.611
-[Debug] d_model=0.4478, d_llm=0.3799
-[Step] Sample=1136 | REJECT | d_model=0.448 | d_total=0.714 | p=0.490
-[Debug] d_model=0.8528, d_llm=0.7288
-[Step] Sample=1139 | REJECT | d_model=0.853 | d_total=1.363 | p=0.256
-[Debug] d_model=0.9199, d_llm=0.7906
-[Step] Sample=1158 | ACCEPT | d_model=0.920 | d_total=1.473 | p=0.229
-[Debug] d_model=0.9107, d_llm=-0.7874
-[Step] Sample=1167 | REJECT | d_model=0.911 | d_total=0.360 | p=0.698
-[Debug] d_model=1.2619, d_llm=1.0879
-[Step] Sample=1185 | REJECT | d_model=1.262 | d_total=2.023 | p=0.132
-[Debug] d_model=1.2806, d_llm=-0.1760
-[Step] Sample=1198 | REJECT | d_model=1.281 | d_total=1.157 | p=0.314
-
-[LLM Summary]
-LLM used  : 121/1200
-Accepted  : 54/1198
-T         : 0.9234
-[LLM Done]
-
-
-Physical Refinement Diagnostics: 0.5273944139480591
-Binder cumulant (1): 0.6466398239135742
-Chi improvement: -0.12519758939743042
-Binder improvement: 0.00023663043975830078
-
-[LLM] Start | B=1200 | T=0.9234
-[Debug] ESS=2.28 | k=1197 | deltaE_band=[0.00,2.00] | in_band=126 | selected=126
-[Debug] d_model=0.4037, d_llm=0.3516
-[Step] Sample=1 | ACCEPT | d_model=0.404 | d_total=0.650 | p=0.522
-[Debug] d_model=1.9562, d_llm=-0.1746
-[Step] Sample=8 | REJECT | d_model=1.956 | d_total=1.834 | p=0.160
-[Debug] d_model=1.2119, d_llm=1.0257
-[Step] Sample=18 | REJECT | d_model=1.212 | d_total=1.930 | p=0.145
-[Debug] d_model=1.5275, d_llm=0.4780
-[Step] Sample=20 | REJECT | d_model=1.527 | d_total=1.862 | p=0.155
-[Debug] d_model=0.2664, d_llm=0.2264
-[Step] Sample=21 | ACCEPT | d_model=0.266 | d_total=0.425 | p=0.654
-[Debug] d_model=0.6343, d_llm=0.5451
-[Step] Sample=23 | ACCEPT | d_model=0.634 | d_total=1.016 | p=0.362
-[Debug] d_model=1.6981, d_llm=-1.0786
-[Step] Sample=58 | ACCEPT | d_model=1.698 | d_total=0.943 | p=0.389
-[Debug] d_model=1.5121, d_llm=1.2660
-[Step] Sample=60 | REJECT | d_model=1.512 | d_total=2.398 | p=0.091
-[Debug] d_model=1.6630, d_llm=-0.1304
-[Step] Sample=61 | REJECT | d_model=1.663 | d_total=1.572 | p=0.208
-[Debug] d_model=0.9966, d_llm=0.8269
-[Step] Sample=77 | REJECT | d_model=0.997 | d_total=1.575 | p=0.207
-[Debug] d_model=1.1034, d_llm=0.9177
-[Step] Sample=110 | REJECT | d_model=1.103 | d_total=1.746 | p=0.175
-[Debug] d_model=0.2852, d_llm=0.2407
-[Step] Sample=133 | REJECT | d_model=0.285 | d_total=0.454 | p=0.635
-[Debug] d_model=0.7188, d_llm=0.6123
-[Step] Sample=135 | REJECT | d_model=0.719 | d_total=1.147 | p=0.317
-[Debug] d_model=0.6523, d_llm=-0.5615
-[Step] Sample=158 | ACCEPT | d_model=0.652 | d_total=0.259 | p=0.772
-[Debug] d_model=1.1273, d_llm=-0.7963
-[Step] Sample=162 | ACCEPT | d_model=1.127 | d_total=0.570 | p=0.566
-[Debug] d_model=1.1083, d_llm=0.9562
-[Step] Sample=176 | REJECT | d_model=1.108 | d_total=1.778 | p=0.169
-[Debug] d_model=1.3513, d_llm=-0.9831
-[Step] Sample=204 | ACCEPT | d_model=1.351 | d_total=0.663 | p=0.515
-[Debug] d_model=1.3730, d_llm=1.1715
-[Step] Sample=218 | ACCEPT | d_model=1.373 | d_total=2.193 | p=0.112
-[Debug] d_model=1.8477, d_llm=0.5250
-[Step] Sample=229 | ACCEPT | d_model=1.848 | d_total=2.215 | p=0.109
-[Debug] d_model=0.0082, d_llm=-0.0070
-[Step] Sample=231 | REJECT | d_model=0.008 | d_total=0.003 | p=0.997
-[Debug] d_model=1.6404, d_llm=-1.3714
-[Step] Sample=232 | REJECT | d_model=1.640 | d_total=0.680 | p=0.506
-[Debug] d_model=0.6976, d_llm=0.5890
-[Step] Sample=239 | REJECT | d_model=0.698 | d_total=1.110 | p=0.330
-[Debug] d_model=1.3203, d_llm=-1.1108
-[Step] Sample=242 | REJECT | d_model=1.320 | d_total=0.543 | p=0.581
-[Debug] d_model=1.5878, d_llm=-1.3204
-[Step] Sample=249 | ACCEPT | d_model=1.588 | d_total=0.664 | p=0.515
-[Debug] d_model=0.5723, d_llm=0.4816
-[Step] Sample=255 | REJECT | d_model=0.572 | d_total=0.909 | p=0.403
-[Debug] d_model=1.3787, d_llm=-1.1541
-[Step] Sample=260 | ACCEPT | d_model=1.379 | d_total=0.571 | p=0.565
-[Debug] d_model=0.9824, d_llm=-0.4824
-[Step] Sample=264 | REJECT | d_model=0.982 | d_total=0.645 | p=0.525
-[Debug] d_model=1.4280, d_llm=1.1933
-[Step] Sample=266 | ACCEPT | d_model=1.428 | d_total=2.263 | p=0.104
-[Debug] d_model=1.5289, d_llm=-0.7873
-[Step] Sample=280 | REJECT | d_model=1.529 | d_total=0.978 | p=0.376
-[Debug] d_model=0.4846, d_llm=0.4063
-[Step] Sample=285 | REJECT | d_model=0.485 | d_total=0.769 | p=0.463
-[Debug] d_model=1.3068, d_llm=1.0924
-[Step] Sample=291 | REJECT | d_model=1.307 | d_total=2.071 | p=0.126
-[Debug] d_model=0.4756, d_llm=-0.4027
-[Step] Sample=294 | ACCEPT | d_model=0.476 | d_total=0.194 | p=0.824
-[Debug] d_model=1.8405, d_llm=0.1279
-[Step] Sample=302 | REJECT | d_model=1.840 | d_total=1.930 | p=0.145
-[Debug] d_model=1.3846, d_llm=-0.3808
-[Step] Sample=306 | ACCEPT | d_model=1.385 | d_total=1.118 | p=0.327
-[Debug] d_model=0.3527, d_llm=0.2951
-[Step] Sample=317 | REJECT | d_model=0.353 | d_total=0.559 | p=0.572
-[Debug] d_model=1.6268, d_llm=0.4895
-[Step] Sample=318 | REJECT | d_model=1.627 | d_total=1.969 | p=0.140
-[Debug] d_model=1.3159, d_llm=-1.0843
-[Step] Sample=320 | ACCEPT | d_model=1.316 | d_total=0.557 | p=0.573
-[Debug] d_model=1.3474, d_llm=-1.0651
-[Step] Sample=340 | REJECT | d_model=1.347 | d_total=0.602 | p=0.548
-[Debug] d_model=1.7160, d_llm=-1.3887
-[Step] Sample=343 | ACCEPT | d_model=1.716 | d_total=0.744 | p=0.475
-[Debug] d_model=0.9579, d_llm=-0.7445
-[Step] Sample=350 | REJECT | d_model=0.958 | d_total=0.437 | p=0.646
-[Debug] d_model=1.2210, d_llm=-0.9943
-[Step] Sample=356 | ACCEPT | d_model=1.221 | d_total=0.525 | p=0.592
-[Debug] d_model=1.9958, d_llm=-0.6493
-[Step] Sample=365 | REJECT | d_model=1.996 | d_total=1.541 | p=0.214
-[Debug] d_model=0.9462, d_llm=-0.6805
-[Step] Sample=381 | ACCEPT | d_model=0.946 | d_total=0.470 | p=0.625
-[Debug] d_model=0.1398, d_llm=-0.1135
-[Step] Sample=387 | ACCEPT | d_model=0.140 | d_total=0.060 | p=0.941
-[Debug] d_model=0.8206, d_llm=-0.1620
-[Step] Sample=407 | ACCEPT | d_model=0.821 | d_total=0.707 | p=0.493
-[Debug] d_model=1.1049, d_llm=0.3080
-[Step] Sample=413 | REJECT | d_model=1.105 | d_total=1.320 | p=0.267
-[Debug] d_model=1.2571, d_llm=0.0319
-[Step] Sample=423 | REJECT | d_model=1.257 | d_total=1.279 | p=0.278
-[Debug] d_model=0.6647, d_llm=0.5513
-[Step] Sample=438 | REJECT | d_model=0.665 | d_total=1.051 | p=0.350
-[Debug] d_model=0.0679, d_llm=-0.0572
-[Step] Sample=441 | ACCEPT | d_model=0.068 | d_total=0.028 | p=0.972
-[Debug] d_model=1.2850, d_llm=-1.0793
-[Step] Sample=456 | REJECT | d_model=1.285 | d_total=0.530 | p=0.589
-[Debug] d_model=0.5861, d_llm=-0.1251
-[Step] Sample=468 | ACCEPT | d_model=0.586 | d_total=0.499 | p=0.607
-[Debug] d_model=1.7388, d_llm=1.4518
-[Step] Sample=480 | REJECT | d_model=1.739 | d_total=2.755 | p=0.064
-[Debug] d_model=1.4202, d_llm=-1.1786
-[Step] Sample=483 | ACCEPT | d_model=1.420 | d_total=0.595 | p=0.551
-[Debug] d_model=1.3608, d_llm=-0.5959
-[Step] Sample=500 | REJECT | d_model=1.361 | d_total=0.944 | p=0.389
-[Debug] d_model=1.2350, d_llm=1.0201
-[Step] Sample=504 | REJECT | d_model=1.235 | d_total=1.949 | p=0.142
-[Debug] d_model=1.2442, d_llm=1.0268
-[Step] Sample=523 | REJECT | d_model=1.244 | d_total=1.963 | p=0.140
-[Debug] d_model=1.1492, d_llm=-0.9499
-[Step] Sample=526 | REJECT | d_model=1.149 | d_total=0.484 | p=0.616
-[Debug] d_model=0.6560, d_llm=0.5480
-[Step] Sample=534 | ACCEPT | d_model=0.656 | d_total=1.040 | p=0.354
-[Debug] d_model=1.2942, d_llm=-0.7107
-[Step] Sample=543 | REJECT | d_model=1.294 | d_total=0.797 | p=0.451
-[Debug] d_model=1.3882, d_llm=-1.1510
-[Step] Sample=592 | REJECT | d_model=1.388 | d_total=0.583 | p=0.558
-[Debug] d_model=1.3633, d_llm=1.1256
-[Step] Sample=602 | REJECT | d_model=1.363 | d_total=2.151 | p=0.116
-[Debug] d_model=0.9116, d_llm=0.7576
-[Step] Sample=652 | ACCEPT | d_model=0.912 | d_total=1.442 | p=0.236
-[Debug] d_model=1.7746, d_llm=1.4496
-[Step] Sample=671 | REJECT | d_model=1.775 | d_total=2.789 | p=0.061
-[Debug] d_model=1.2363, d_llm=1.0095
-[Step] Sample=672 | ACCEPT | d_model=1.236 | d_total=1.943 | p=0.143
-[Debug] d_model=1.9001, d_llm=1.3891
-[Step] Sample=684 | ACCEPT | d_model=1.900 | d_total=2.873 | p=0.057
-[Debug] d_model=1.4827, d_llm=1.1788
-[Step] Sample=685 | REJECT | d_model=1.483 | d_total=2.308 | p=0.099
-[Debug] d_model=1.7901, d_llm=-1.4018
-[Step] Sample=688 | REJECT | d_model=1.790 | d_total=0.809 | p=0.445
-[Debug] d_model=1.4796, d_llm=-1.1528
-[Step] Sample=695 | REJECT | d_model=1.480 | d_total=0.673 | p=0.510
-[Debug] d_model=1.5537, d_llm=1.2021
-[Step] Sample=697 | REJECT | d_model=1.554 | d_total=2.395 | p=0.091
-[Debug] d_model=0.7808, d_llm=-0.6100
-[Step] Sample=705 | ACCEPT | d_model=0.781 | d_total=0.354 | p=0.702
-[Debug] d_model=1.6901, d_llm=-0.7006
-[Step] Sample=706 | REJECT | d_model=1.690 | d_total=1.200 | p=0.301
-[Debug] d_model=0.9417, d_llm=-0.7328
-[Step] Sample=709 | ACCEPT | d_model=0.942 | d_total=0.429 | p=0.651
-[Debug] d_model=0.0624, d_llm=-0.0493
-[Step] Sample=716 | ACCEPT | d_model=0.062 | d_total=0.028 | p=0.972
-[Debug] d_model=0.5894, d_llm=-0.4712
-[Step] Sample=717 | ACCEPT | d_model=0.589 | d_total=0.260 | p=0.771
-[Debug] d_model=1.7504, d_llm=-1.3798
-[Step] Sample=725 | REJECT | d_model=1.750 | d_total=0.785 | p=0.456
-[Debug] d_model=0.9109, d_llm=-0.7233
-[Step] Sample=728 | REJECT | d_model=0.911 | d_total=0.405 | p=0.667
-[Debug] d_model=1.1193, d_llm=0.8916
-[Step] Sample=733 | REJECT | d_model=1.119 | d_total=1.743 | p=0.175
-[Debug] d_model=1.6493, d_llm=-0.1578
-[Step] Sample=739 | REJECT | d_model=1.649 | d_total=1.539 | p=0.215
-[Debug] d_model=1.9396, d_llm=1.4987
-[Step] Sample=743 | REJECT | d_model=1.940 | d_total=2.989 | p=0.050
-[Debug] d_model=0.6816, d_llm=-0.5324
-[Step] Sample=745 | ACCEPT | d_model=0.682 | d_total=0.309 | p=0.734
-[Debug] d_model=1.5206, d_llm=-0.2774
-[Step] Sample=751 | REJECT | d_model=1.521 | d_total=1.326 | p=0.265
-[Debug] d_model=1.4355, d_llm=1.0369
-[Step] Sample=752 | REJECT | d_model=1.435 | d_total=2.161 | p=0.115
-[Debug] d_model=0.5978, d_llm=-0.4680
-[Step] Sample=763 | REJECT | d_model=0.598 | d_total=0.270 | p=0.763
-[Debug] d_model=1.2783, d_llm=1.0007
-[Step] Sample=771 | REJECT | d_model=1.278 | d_total=1.979 | p=0.138
-[Debug] d_model=0.4344, d_llm=-0.3447
-[Step] Sample=777 | ACCEPT | d_model=0.434 | d_total=0.193 | p=0.824
-[Debug] d_model=0.4656, d_llm=0.3743
-[Step] Sample=794 | REJECT | d_model=0.466 | d_total=0.728 | p=0.483
-[Debug] d_model=1.2260, d_llm=0.7319
-[Step] Sample=802 | REJECT | d_model=1.226 | d_total=1.738 | p=0.176
-[Debug] d_model=1.0530, d_llm=0.8505
-[Step] Sample=808 | REJECT | d_model=1.053 | d_total=1.648 | p=0.192
-[Debug] d_model=0.3712, d_llm=0.3040
-[Step] Sample=842 | REJECT | d_model=0.371 | d_total=0.584 | p=0.558
-[Debug] d_model=1.1915, d_llm=-0.9766
-[Step] Sample=872 | REJECT | d_model=1.192 | d_total=0.508 | p=0.602
-[Debug] d_model=0.3362, d_llm=0.2794
-[Step] Sample=876 | REJECT | d_model=0.336 | d_total=0.532 | p=0.588
-[Debug] d_model=0.0778, d_llm=0.0657
-[Step] Sample=891 | REJECT | d_model=0.078 | d_total=0.124 | p=0.884
-[Debug] d_model=1.6447, d_llm=-1.3691
-[Step] Sample=915 | REJECT | d_model=1.645 | d_total=0.686 | p=0.503
-[Debug] d_model=1.0312, d_llm=0.8200
-[Step] Sample=921 | ACCEPT | d_model=1.031 | d_total=1.605 | p=0.201
-[Debug] d_model=1.7844, d_llm=1.4646
-[Step] Sample=926 | REJECT | d_model=1.784 | d_total=2.810 | p=0.060
-[Debug] d_model=1.0370, d_llm=0.8547
-[Step] Sample=928 | REJECT | d_model=1.037 | d_total=1.635 | p=0.195
-[Debug] d_model=1.5452, d_llm=-1.2618
-[Step] Sample=934 | REJECT | d_model=1.545 | d_total=0.662 | p=0.516
-[Debug] d_model=0.7448, d_llm=0.1433
-[Step] Sample=960 | ACCEPT | d_model=0.745 | d_total=0.845 | p=0.430
-[Debug] d_model=1.7141, d_llm=-1.3925
-[Step] Sample=972 | ACCEPT | d_model=1.714 | d_total=0.739 | p=0.477
-[Debug] d_model=0.6227, d_llm=0.5116
-[Step] Sample=973 | ACCEPT | d_model=0.623 | d_total=0.981 | p=0.375
-[Debug] d_model=1.1991, d_llm=-0.9856
-[Step] Sample=983 | REJECT | d_model=1.199 | d_total=0.509 | p=0.601
-[Debug] d_model=1.6347, d_llm=1.3277
-[Step] Sample=990 | REJECT | d_model=1.635 | d_total=2.564 | p=0.077
-[Debug] d_model=1.6530, d_llm=1.3267
-[Step] Sample=991 | REJECT | d_model=1.653 | d_total=2.582 | p=0.076
-[Debug] d_model=1.5552, d_llm=-1.2379
-[Step] Sample=993 | REJECT | d_model=1.555 | d_total=0.689 | p=0.502
-[Debug] d_model=1.7169, d_llm=1.2795
-[Step] Sample=994 | REJECT | d_model=1.717 | d_total=2.612 | p=0.073
-[Debug] d_model=1.8759, d_llm=1.4488
-[Step] Sample=996 | REJECT | d_model=1.876 | d_total=2.890 | p=0.056
-[Debug] d_model=0.0549, d_llm=0.0430
-[Step] Sample=997 | ACCEPT | d_model=0.055 | d_total=0.085 | p=0.919
-[Debug] d_model=0.2541, d_llm=0.2022
-[Step] Sample=1005 | ACCEPT | d_model=0.254 | d_total=0.396 | p=0.673
-[Debug] d_model=1.8164, d_llm=1.4223
-[Step] Sample=1016 | REJECT | d_model=1.816 | d_total=2.812 | p=0.060
-[Debug] d_model=1.4372, d_llm=1.1209
-[Step] Sample=1017 | REJECT | d_model=1.437 | d_total=2.222 | p=0.108
-[Debug] d_model=1.3734, d_llm=-1.0688
-[Step] Sample=1018 | REJECT | d_model=1.373 | d_total=0.625 | p=0.535
-[Debug] d_model=1.9858, d_llm=-0.3743
-[Step] Sample=1028 | REJECT | d_model=1.986 | d_total=1.724 | p=0.178
-[Debug] d_model=1.9464, d_llm=-0.6565
-[Step] Sample=1037 | REJECT | d_model=1.946 | d_total=1.487 | p=0.226
-[Debug] d_model=0.7186, d_llm=0.5441
-[Step] Sample=1077 | REJECT | d_model=0.719 | d_total=1.100 | p=0.333
-[Debug] d_model=1.0615, d_llm=-0.0115
-[Step] Sample=1079 | REJECT | d_model=1.062 | d_total=1.053 | p=0.349
-[Debug] d_model=0.6014, d_llm=-0.4634
-[Step] Sample=1082 | ACCEPT | d_model=0.601 | d_total=0.277 | p=0.758
-[Debug] d_model=0.5787, d_llm=0.4513
-[Step] Sample=1119 | ACCEPT | d_model=0.579 | d_total=0.895 | p=0.409
-[Debug] d_model=1.4818, d_llm=1.1498
-[Step] Sample=1128 | REJECT | d_model=1.482 | d_total=2.287 | p=0.102
-[Debug] d_model=0.3193, d_llm=-0.2514
-[Step] Sample=1133 | ACCEPT | d_model=0.319 | d_total=0.143 | p=0.866
-[Debug] d_model=1.3218, d_llm=-1.0391
-[Step] Sample=1150 | ACCEPT | d_model=1.322 | d_total=0.594 | p=0.552
-[Debug] d_model=0.4108, d_llm=-0.3274
-[Step] Sample=1157 | ACCEPT | d_model=0.411 | d_total=0.182 | p=0.834
-[Debug] d_model=0.5729, d_llm=-0.2024
-[Step] Sample=1161 | ACCEPT | d_model=0.573 | d_total=0.431 | p=0.650
-[Debug] d_model=0.9603, d_llm=0.7793
-[Step] Sample=1168 | REJECT | d_model=0.960 | d_total=1.506 | p=0.222
-[Debug] d_model=1.0141, d_llm=0.2374
-[Step] Sample=1170 | ACCEPT | d_model=1.014 | d_total=1.180 | p=0.307
-[Debug] d_model=1.4733, d_llm=-0.9457
-[Step] Sample=1189 | ACCEPT | d_model=1.473 | d_total=0.811 | p=0.444
-[Debug] d_model=0.4131, d_llm=0.3392
-[Step] Sample=1194 | REJECT | d_model=0.413 | d_total=0.651 | p=0.522
-
-[LLM Summary]
-LLM used  : 126/1200
-Accepted  : 47/1197
-T         : 0.9234
-[LLM Done]
-
-
-Secondary magnetic susceptibility: 0.5301476716995239
-Binder cumulant (2): 0.6474097967147827
-Chi improvement: -0.12611085176467896
-Binder improvement: 8.07642936706543e-05
-
-Running energy analysis ↓
-
-
-Running diagnostics with the following calculations ↙
-
-✔ Energy distribution finished in 286.21 seconds
-✔ Energy landscape extremes finished in 0.12 seconds
-✔ Phase diagram finished in 1.80 seconds
-✔ RBM filters finished in 0.08 seconds
-✔ Sample quality metrics finished in 27.39 seconds
-
-Diagnostics completed in 317.12 seconds
-
-GPU Energy Used : 265258.02 Joules
-
-Device: cuda
-, ↘
-
-samples_gpu0_seed1_refined.png → Patches: torch.Size([1200, 1, 28, 28])
-samples_gpu0_seed1_refined.png → Avg MSE: 0.11792941
-Saved: samples_gpu0_seed1_perfect.png
-
-Device: cuda
-, ↘
-
-samples_prof_gpu0_seed1_refined.png → Patches: torch.Size([1200, 1, 28, 28])
-samples_prof_gpu0_seed1_refined.png → Avg MSE: 0.11838679
-Saved: samples_prof_gpu0_seed1_perfect.png
-
-DEBUG perfect_img_path: samples_prof_gpu0_seed1_perfect.png
-[IMAGE LPIPS] 0.1402321457862854
-
-[LLM RAW OUTPUT]
- {
-"analysis": "High image_similarity (0.86) and low reconstruction entropy (0.156) indicate that X preserves the structural manifold defined by Y — modes are intact and there is no structural degradation. Learning is active (learning_signal 0.328, learning_active = true, stagnation = false) while the system is heating slightly (temp = 0.923, temp_slope > 0). Mixing shows mixed signals: r_hat ~1.008 (near ideal) but tau_int ~6.9 (slow autocorrelation). Quality metrics (low mean L2, high effective rank) support good reconstruction and representational capacity. Given the strong structural similarity, the STRICT CONSTRAINT forbids labeling this as mode collapse despite somewhat slow mixing and low entropy. Overall: the system is actively learning and producing ordered, structurally faithful outputs. Confidence is bounded by the strong image_similarity evidence but tempered by limited temporal-history detail and the slow mixing signal.",
-"regime": "learning",
-"phase": "ordered",
-"scores": {
-  "temperature": 0.92,
-  "gibbs": 0.72
-},
-"risk": {
-  "stagnation": 0.05,
-  "collapse": 0.01,
-  "over_ordering": 0.12
-},
-"confidence": 0.72
-}
-[SYS] EVIDENCE=0.272 | LLM=0.720 → FINAL=0.272
-
-[LLM CLEAN RESULT]
- {'analysis': 'High image_similarity (0.86) and low reconstruction entropy (0.156) indicate that X preserves the structural manifold defined by Y — modes are intact and there is no structural degradation. Learning is active (learning_signal 0.328, learning_active = true, stagnation = false) while the system is heating slightly (temp = 0.923, temp_slope > 0). Mixing shows mixed signals: r_hat ~1.008 (near ideal) but tau_int ~6.9 (slow autocorrelation). Quality metrics (low mean L2, high effective rank) support good reconstruction and representational capacity. Given the strong structural similarity, the STRICT CONSTRAINT forbids labeling this as mode collapse despite somewhat slow mixing and low entropy. Overall: the system is actively learning and producing ordered, structurally faithful outputs. Confidence is bounded by the strong image_similarity evidence but tempered by limited temporal-history detail and the slow mixing signal. [OVERRIDDEN: high structural similarity]', 'regime': 'learning', 'phase': 'ordered', 'scores': {'temperature': 0.46, 'gibbs': 0.36}, 'risk': {'stagnation': 0.05, 'collapse': 0.0, 'over_ordering': 0.12}, 'confidence': 0.2719535708427429, 'evidence': 0.2719535708427429, 'llm_conf_raw': 0.72, 'reason': 'low_confidence_scaled'}
-[ANASIS] collapse=0.167 stagnation=0.577 healthy_align=0.411 → risk=0.548
-[ANALYSIS SIGNAL] 0.548
-[LLM OUTPUT SAVED] → llm_output_gpu0_seed1.json
-
-[LLM RESULT]
-
-{
-  "analysis": "High image_similarity (0.86) and low reconstruction entropy (0.156)
-indicate that X preserves the structural manifold defined by Y — modes are intact and
-there is no structural degradation. Learning is active (learning_signal 0.328,
-learning_active = true, stagnation = false) while the system is heating slightly (temp =
-0.923, temp_slope > 0). Mixing shows mixed signals: r_hat ~1.008 (near ideal) but tau_int
-~6.9 (slow autocorrelation). Quality metrics (low mean L2, high effective rank) support
-good reconstruction and representational capacity. Given the strong structural similarity,
-the STRICT CONSTRAINT forbids labeling this as mode collapse despite somewhat slow mixing
-and low entropy. Overall: the system is actively learning and producing ordered,
-structurally faithful outputs. Confidence is bounded by the strong image_similarity
-evidence but tempered by limited temporal-history detail and the slow mixing signal.
-[OVERRIDDEN: high structural similarity]",
-  "regime": "learning",
-  "phase": "ordered",
-  "scores": {
-    "temperature": 0.46,
-    "gibbs": 0.36
-  },
-  "risk": {
-    "stagnation": 0.05,
-    "collapse": 0.0,
-    "over_ordering": 0.12
-  },
-  "confidence": 0.2719535708427429,
-  "evidence": 0.2719535708427429,
-  "llm_conf_raw": 0.72,
-  "reason": "low_confidence_scaled"
-}
-Hybrid Thermodynamic RBM Final Results
-Seed: 1 | GPU: 0
-Final Temperature        : 0.923444
-Weight Norm              : 68.468369
-Spectral Gain            : 74.144559
-Train Log-Likelihood     : -411.152252
-Test Log-Likelihood      : -410.739502
-Train Pseudo-Likelihood  : -0.678435
-Test Pseudo-Likelihood   : -0.678006
-Reconstruction MSE       : 0.015900
-Reconstruction Accuracy  : 0.980278
-Mean Data Energy         : -652.020264
-Mean Model Energy        : -634.357605
-Energy Gap               : 17.662659
-GPU Energy Used          : 265258.02 J
-Log Partition (AIS)      : 1063.172485
-AIS ESS                  : 2483.00
-AIS Log-Weight Variance  : 1.972837
-MCMC tau_int             : 6.89
-MCMC tau_std             : 0.46
-MCMC tau_max             : 8.41
-MCMC tau_min             : 5.63
-MCMC tau_rms             : 6.91
-MCMC tau_geo             : 6.88
-MCMC tau_harm            : 6.86
-MCMC ESS                 : 29141484.00
-MCMC ESS / chain         : 14570.74
-MCMC R-hat               : 1.0086
-MCMC ACF Length          : 17
-MCMC Mix Index           : 0.410697
-Entropy Band             : Closed interval [0.410154, 0.433570]
-Choice(Mix | Band)       : C(0.410697 | [0.410154, 0.433570]) = 0.410697
-Band Consistency         : True
-Hamming                  : 0.198923
-Effective Rank           : 90.309441
-Mean Distribution L2     : 0.100056
-Professional Samples File: samples_prof_gpu0_seed1.png
-Generated Samples File   : samples_gpu0_seed1.png
-
-(srtrbm_env) ...:~/srtrbm_project$